Assessment of Multivariate Neural Time Series by Phase Synchrony Clustering in a Time-Frequency-Topography Representation.

Most EEG phase synchrony measures are of bivariate nature. Those that are multivariate focus on producing global indices of the synchronization state of the system. Thus, better descriptions of spatial and temporal local interactions are still in demand. A framework for characterization of phase synchrony relationships between multivariate neural time series is presented, applied either in a single epoch or over an intertrial assessment, relying on a proposed clustering algorithm, termed Multivariate Time Series Clustering by Phase Synchrony, which generates fuzzy clusters for each multivalued time sample and thereupon obtains hard clusters according to a circular variance threshold; such cluster modes are then depicted in Time-Frequency-Topography representations of synchrony state beyond mere global indices. EEG signals from P300 Speller sessions of four subjects were analyzed, obtaining useful insights of synchrony patterns related to the ERP and even revealing steady-state artifacts at 7.6 Hz. Further, contrast maps of Levenshtein Distance highlight synchrony differences between ERP and no-ERP epochs, mainly at delta and theta bands. The framework, which is not limited to one synchrony measure, allows observing dynamics of phase changes and interactions among channels and can be applied to analyze other cognitive states rather than ERP versus no ERP.

Análisis electroencefalográfico de la conectividad funcional en habituación por teoría de gráficas / Electroencephalographic analysis of the functional conectivity in habituation by graphics theory

Resumen Se ha propuesto, en la literatura de neurociencias, la hipótesis de que el cerebro funciona con base en redes glioneuronales, responsables de los procesos sensoriales, motores y cognitivos. La teoría de gráficas ofrece modelos matemáticos para describir estas redes complejas a partir de simples abstracciones: nodos y aristas. En este trabajo se plantea el uso de técnicas de procesamiento de señales para el análisis de la conectividad funcional entre ensambles neuronales a partir de medidas derivadas del EEG a través de la teoría de gráficas. Se describen los procedimientos para el análisis de la conectividad, desde el procesamiento y acondicionamiento de los registros, el cálculo de la densidad espectral en el EEG en banda ancha, la determinación de medidas de conectividad entre electrodos, y la estimación de parámetros de las redes complejas resultantes. Se analizó el EEG de 50 sujetos en intervalos de antes (Pre) y durante la fotoestimulación repetida (Rph). Con base en la significancia estadística de la respuesta durante Rph de desincronización/sincronización (D/S) en la banda alfa, y su extensión en las regiones corticales, los participantes se separaron en tres grupos: G1, G2 y G3. La pendiente de la regresión lineal D/S vs repetición en banda alfa, fue significativa en 16 de 16 regiones en G1, 12 de 16 en G2 y 5 de 16 en G3. La conectividad funcional siguió estos cambios mientras que en las bandas delta, theta y beta no se registraron modificaciones. D se relaciona con activación y la S con inhibición lo que apoya la hipótesis de la participación de alfa en el proceso cognitivo de habituación a la fotoestimulación.

Abstract The hypothesis that the brain works using glial-neuronal networks that are responsible for sensory, motor and cognitive processes has been proposed in the neuroscience literature. Graph theory offers mathematical models to describe these complex networks from simple abstractions such as nodes and connecting edges. In this work, usign signal processing techniques to the analysis of functional connectivity between neuronal emsembles derived from EEG measurements through theory graph. Procedures for the analysis of connectivity are described, from multichannel record processing and conditioning, computation of wideband power spectral density, determination of connectivity measures between electrodes, and parameter estimation of the resulting complex networks. EEG recordings from fifty subjects were analyzed in intervals before (pre) and during repeated photostimulation (Rph). Based on the statistical significance of the response during Rph, desynchronization/synchronization (D/S) in alfa band, and its wide distribution in corticals areas, the participants were divided into three groups: G1 , G2 and G3. The slope D/S in alfa band was significant in 16 of 16 regions (G1), 12 of 16 (G2) and 5 of 16 (G3). The functional connectivity depicted the same trend as these changes, whereas in the delta, theta and beta bands occurred no modifications. D is related to activation and S with inhibition, supporting the hypothesis of alfa-band's participation in the cognitive process of habituation to photostimulation.

Optimized Detection of the Infrequent Response in P300-Based Brain-Computer Interfaces / Detección optimizada de la respuesta infrecuente en interfaces cerebro-computadora

This paper presents an application developed on the BCI2000 platform which reduces the average spelling time per symbol on the Donchin speller. The motivation was to reduce the compromise between spelling rate and spelling accuracy due to the large amount of responses required in order to perform coherent average techniques. The methodology was made under a Bayesian approach which allows calculation of each target's class posterior probability. This result indicates the probability of each response of belonging to the infrequent class. When there is enough evidence to make a decision the system stops the stimulation process and moves on with the next symbol, otherwise it continues stimulating the user until it finds the selected letter. The average spelling rate, after using the proposed methodology with 14 healthy users and a maximum number of 5 stimulation sequences, was of 6.1 ± 0.63 char/min, compared to a constant rate of 3.93 char/min with the standard system.

Este trabajo presenta una aplicación desarrollada sobre la plataforma BCI2000 que disminuye el tiempo promedio de selección de los símbolos del deletreador de Donchin. La motivación consistió en reducir el compromiso entre la taza de deletreo y la precisión correspondiente, la cual surge como consecuencia de la gran cantidad de respuestas necesarias para realizar técnicas de promediación coherente. La metodología propuesta se basa en un enfoque Bayesiano que permite calcular la probabilidad posterior asociada con la clasificación de cada objetivo, resultado que indica la evidencia que presentan las respuestas de pertenecer a la clase infrecuente. Cuando existe evidencia suficiente para tomar una decisión, el sistema detiene el proceso de estimulación y continúa con el siguiente símbolo, de lo contrario permanece estimulando al usuario hasta conseguir identificar la letra seleccionada. Después de utilizar la metodología propuesta sobre los registros de 14 usuarios sanos con un número máximo de 5 series de estimulación, el tiempo promedio de deletreo reportado es de 6.1 ± 0.63 letras/min, el cual es comparado con una taza constante de 3.93 letras/min obtenido con un sistema convencional.

Implementation of a real-time algorithm for maternal and fetal heart rate monitoring in a digital signal controller platform.

An LMS-based algorithm to monitor fetal and maternal heart rate in real time was implemented and evaluated on a development platform. Hardware has three modules: dsPIC30F digital signal controller, a low-noise analog front end and a storage stage. They were evaluated using on-chip debugging tools and a patient simulator. Algorithm performance was tested using simulation tools and real data. Other measures like process run-times and power consumption, were analyzed to evaluate the design feasibility. Dataset was conformed by 25 annotated records from different gestational age pregnant women. Sensitivity and accuracy were used as performance measures. In general, sensitivity was high for maternal (95.3%) and fetal (87.1%) detections. Results showed that the chosen architecture can run efficiently the algorithm processes, obtaining high detection rates under appropriate SNR conditions.

Texture-based echocardiographic segmentation using a non-parametric estimator and an active contour model.

An accurate segmentation of cardiac cavities is necessary to assess cardiac function and to determine quantitative parameters. Several semi-automatic techniques have been tested to achieve this goal. In this work we propose an algorithm to segment cardiac structures, based on a robust pre-processing step that eliminates noise and extracts an initial frontier, together with a refined deformable model, that integrates edge confidence and texture information. Results show that a combination of a mean-shift filter with an active contour model is adequate for echographic images, especially when texture information is included.